Searching through databases of media items such as stock photographs is a task often undertaken both in the media industry and by consumers generally. Websites such as gettyimages.com images.google.com and others typically provide searching methods that involve descriptor based indexing of image content. The content descriptors typically relate to aspects such as concept, emotion, location or to the characteristics of people in the image. The user specifies values of one or more content descriptors typically using checkboxes in drop-down lists and the image search engine then searches the image database index for images matching the user-specified values. Matching images are then typically displayed, accompanied with technical information such as resolution or origin, in a scrollable window. Usually a first page shows the closest matches, which may involve some assessment of additional native properties such as colour balance, and multiple further pages contain less close matches. The user inspects the many images and may refine the search by specifying further content descriptor values, or by choosing one reference image as an example.
Although an initial search using descriptors produces many potentially relevant images, refinement of the search to select out images of more interest is often frustrating. Further, the need to scroll through and individually examine a mixture of a large number of images and text is tiring and tedious.
There is therefore a need to provide improved database searching techniques that enable more efficient and user-friendly identification of useful data items, with particular application to media.
The inventors of the current invention have found a much improved method of assisting the user to locate a data item of interest, by making advances in the use of reference data items and ordered and manageable display of search results.